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  pretty_name: MoleculeNet ToxCast
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  size_categories:
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  - 1K<n<10K
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  pretty_name: MoleculeNet ToxCast
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  size_categories:
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  - 1K<n<10K
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+ ---
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+
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+ # MoleculeNet ToxCast
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+
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+ ToxCast dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through
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+ [scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library.
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+
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+ The task is to predict 617 toxicity targets from a large library of compounds based on in vitro high-throughput screening. All tasks are binary.
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+
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+ Note that targets have missing values. Algorithms should be evaluated only on present labels. For training data, you may want to impute them, e.g. with zeros.
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+
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+ | **Characteristic** | **Description** |
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+ |:------------------:|:------------------------:|
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+ | Tasks | 617 |
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+ | Task type | multitask classification |
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+ | Total samples | 8576 |
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+ | Recommended split | scaffold |
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+ | Recommended metric | AUROC |
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+
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+ ## References
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+ <a id="1">[1]</a>
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+ Ann M. Richard et al.
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+ "ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology"
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+ Chem. Res. Toxicol. 2016, 29, 8, 1225–1251
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+ https://pubs.acs.org/doi/10.1021/acs.chemrestox.6b00135>
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+
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+ <a id="2">[2]</a>
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+ Wu, Zhenqin, et al.
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+ "MoleculeNet: a benchmark for molecular machine learning."
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+ Chemical Science 9.2 (2018): 513-530
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+ https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a